This is a post based on recent tutorials I published, with the goal of discussing how to prepare your current MySQL instance to be configured as an External Primary Server with a Replica/Follower into Google Cloud Platform.

First, I want to talk about the jargon used here. I will be using primary to represent the external “master” server, and replica to represent the “slave” server. Personally, I prefer the terms leader/follower but primary/replica currently seems to be more common in the industry. At some point, the word slave will be used, but because it is the keyword embedded on the server to represent a replica.

The steps given will be in the context of a VM running a one-click install of WordPress acquired through the Google Marketplace (formerly known as Launcher) .

To help prepare for replication you need to configure your primary to meet some requirements.

server-id must be configured; it needs to have binary logging enabled; it needs to have GTID enabled, and GTID must be enforced. Tutorial.

A dump file must be generated using the mysqldump command with some information on it.

The steps above are also necessary if you are migrating from another cloud or on-prem.

Why split the application and database and use a service like Cloud SQL?

Cloud SQL

First, you will be able to use your application server to do what it was mainly designed for: serve requests of your WordPress application (and it doesn’t much matter for the purposes of this post if you are using nginx or Apache).

Databases are heavy, their deadly sin is gluttony, they tend to occupy as much memory as they can to make lookups fairly fast. Once you are faced with this reality, sharing resources with your application is not a good idea.

Next, you may say: I could use Kubernetes! Yes, you could, but just because you can do something doesn’t mean you should. Configuring stateful applications inside Kubernetes is a challenge, and the fact that pods can be killed at any moment may pose a threat to your data consistency if it happens mid transaction. There are solutions on the market that use MySQL on top of Kubernetes, but that would be a totally different discussion.

You also don’t need to use Cloud SQL, you can set up your
database replicas, or even the primary, on another VM (still wins when compared with putting the database and application together), but in this scenario you are perpetually risking hitting the limits of your finite hardware capabilities.

Finally, Cloud SQL has a 99.95% availability and it is curated by the SRE team of Google. That means you can focus your efforts on what really matters — developing your application — and not spend hours, or even days, setting up servers. Other persuasively convenient features include PITR (Point in Time Recovery) and High Availability in case a failover is necessary.

Setting up the replica on GCP

Accessing the menu SQL in your Google Cloud Console will give you a listing of your current Cloud SQL instances. From there execute the following:

Click on the Migrate Data button

Once you have familiarized yourself with the steps shown on the screen, click on Begin Migration

In the Data source details , fill the form out as follows:

Name of data source: Any valid name for a Cloud SQL instance that will represent the primary server name

The next section Cloud SQL read replica creation, will allow you to choose:

Read replica instance ID: Any valid name for a Cloud SQL instance that will represent the replica server name

Location: choose the Region and then the Zone for which your instance will be provisioned.

Machine Type: Choose a Machine Type for your replica; This can be modified later! In some cases it is recommended to choose a higher instance configuration than what you will keep after replication synchronization finishes

Storage capacity: It can be from 10GB up to 10TB. The checkbox for Enable automatic storage increases means whenever you’re near capacity, space will be incrementally increased. All increases are permanent

(Optional) More options can be configured by clicking on Show advanced options like Authorized networks, Database flags, and Labels.

Once you’ve filled out this information, click on Create.

The following section, Data synchronization, will display the previous selected options as well the Outgoing IP Address which must be added to your current proxy, firewall, white-list to be able to connect and fetch replication data. Once you are sure your primary can be accessed using the specified credentials, and the IP was white-listed, you can click on Next. After that replication will start.

Live demo

If you want to see this feature in action, please check this video from Google Cloud Next 2018:

Introduction

What is the X-DevApi? From insidemysql.com there is a definition of the X-DevAPI and its features in the following paragraphs:

The X DevAPI is the common client-side API used by all connectors to abstract the details of the X Protocol. It specifies the common set of CRUD-style functions/methods used by all the official connectors to work with both document store collections and relational tables, a common expression language to establish query properties such as criteria, projections, aliases, and a standard set of additional database management features for handling things like transactions, indexes, etc.

The fact that most of these features share the same format and API between connectors, makes the X DevAPI a perfect fit for modern polyglot development environments such as microservices, and the fact that they are based on a well-documented format allows advanced users to extend client-side implementations and build new middleware components or extensions tailor-made for their use case.

With MySQL 8.0, the X-DevAPI can be used either with the MySQL Shell, or with the MySQL Connectors that supports the X-Protocol. For this blogpost I will be showing you code using the Node.js driver as interface with it.

As of the moment of writing of this post, the latest version for the driver (available on npm) is the 8.0.11.

Hands-on, the relational way

You can use the X-DevAPI to use it in a more “relational” way, consider a table teams from the worldcup schema:

DESCRIBE teams;

Description:

Field

Type

Null

Key

Default

Extra

id

int(11)

NO

PRI

NULL

auto_increment

name

varchar(255)

NO

NULL

players

json

YES

NULL

Notice that the field players is a JSON and it can be NULL.

Returns on a browser (data clipped):

Or on your terminal:

Extra padding on strings

This looks handy, specially if you are working as a document store (which I will talk in a bit), however there are some issues with JSON and VARCHAR fields for now:

VARCHAR fields are getting padded. Take a look at the third line, where it is “Brazil” actually has trailing spaces. Notice on the terminal is how far off the closing quote is.

JSON does work, but a bit in a limited way, in this case, it is only bringing the result set because the column is set to be possibly NULL, on the other hand if you execute the following you will have trouble:

ALTER TABLE teams MODIFY players JSON NOT NULL;

Error on NOT NULL for JSON columns

See how the error is in a string parser that tries to concatenate whatever to it. On my tests usually is empty space, I had zeroes padded on the right too (yes zero on a string), problem 1 looks like is causing problem 2.

They are aware of it, and apparently it will be fixed on a future release.

Hands-on, the document store way

MySQL 8.0 has been marketed extensively as an alternative for NoSQL, although you can actually use JSON with it, there are some caveats to the feature.

I restructured data to be inside a collection instead of a table.

To query it now, you do the following:

Something feels wrong to me with this approach of having to write strings instead of dealing with JavaScript objects inside the .find() method, using the “literal” word for OR and AND. I think there is room for improvement here. At this point I am sorry to say this looks more like as an hasty ORM put together than a real document store interface.

By the way, problem 1 and problem 2 don’t happen here:

Result set for a query

Should I use it now?

I would recommend to wait a bit more. The current version is the first General Availability version, which means next one will have bug fixes and more stability added to it. This definitely is a nifty way for those using Node.js to be able to manipulate data inside MySQL more “natively”, with that in mind, I still feel that I am actually writing SQL to be able to use it the “document store” way.

Bonus

You can actually use the X-DevAPI on Google Cloud Functions.

You need to add the @mysql/xdevapi to package.json and put the code above inside a “wrapper” that looks like this:

Keep in mind that .execute() doesn’t return a promise, but rather receives a callback function to do your data processing of each individual row. This could be a bit annoying for you.

Image header of the post belongs to Pexel. And yes, I know the code shown is not SQL or JavaScript 🙂

A lot of people ask me to teach them how to do query analysis and performance. The truth is: there isn’t a script to follow. The following paragraphs are a brain dump on what usually goes on my mind when I am debugging and analyzing.

Please comment on what you think I should focus on to cover here.

TL; DR;

It’s just a messy post with database-y stuff

This post doesn’t have a conclusion, it is just me laying down my thoughts on performance and optimizations.

Thoughts

Query performance is a really difficult subject to talk about. Mostly because because SQL is a declarative language, leaving it up to the Optimizer to decide which way is the best to retrieve the information needed and that is based in so many variables.

The most common problem regarding optimization I see, comes not from the Database itself, but how we handle the requests on the application layer, the following for instance would cause N+1 problems:

Code example:

Although seemingly innocent at first, this code could easily slow down performance on the database due to the amount of requests that would be made.

You also need to know about the intricacies of indexes, which one is the best, if you have a composite index, which should go first, and what happens if I only use one of the fields of a two column indexes in my search? Does it still uses the index somehow? Another rule of thumb is that if an index is a BTREE, on a single column, you can use it either ASC​ or DESC.

Or better yet: why my transactions are taking so long to complete? Does it have too many indexes on the table? Is any other query locking table X?

Even a single ​INNER JOIN could be highly costly if joining two large tables.

Why are you saving that JSON in a TEXT​ field? Since we are on the subject, you really need the JSON in the relational database and not in a document store?

You don’t need to port all your data from PostgreSQL/MySQL to MongoDB if you want to have MongoDB on your stack. Everything has its place, relational data on relational databases and non-relational data on non relational databases. I even find unfair benchmarks between a SQL database and a NoSQL one. They were made to solve different problems, you can’t possibly have the same use case for both of them.

No, it’s not ok to have category_1, category_2, ..., category_n as columns on your products table.

Relationships should also explicitly live on the RDBMS, not only on your model, if you have a user_id​ on your addresses​ table, tell the database so, naming it user_id doesn’t automatically create the foreign key.

You need:

Or your migration should look something like this:

Line 24: adds to the table addresses​ a foreign key from users.

End

And you, what you think is missing in this blogpost? What do you want to get deeper on?